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Create a Recursive Forecast Learner

Source: R/recursive_forecaster.R
recursive_forecaster.Rd

Function to create a RecursiveForecaster object. This is the recommended way to construct a recursive forecaster. It is a thin wrapper around RecursiveForecaster$new().

A recursive forecaster trains a single regression model and forecasts iteratively one step ahead, feeding each prediction back as a lag/rolling feature for the next step. For the direct strategy (one model per horizon) see direct_forecaster().

Usage

recursive_forecaster(
  learner,
  lags = NULL,
  id = NULL,
  param_vals = list(),
  predict_type = NULL,
  clone_graph = TRUE
)

Arguments

learner

(mlr3::Learner | mlr3pipelines::Graph | mlr3pipelines::PipeOp)
A regression learner (when lags is provided) or a graph/PipeOp.

lags

(integer() | NULL)
The lag values to use for creating lag features. If provided, learner is wrapped with po("fcst.lags", lags = lags). If NULL, learner must be a mlr3pipelines::Graph or mlr3pipelines::PipeOp.

id

(character(1) | NULL)
Identifier, default NULL (auto-generated).

param_vals

(named list())
List of hyperparameter settings.

predict_type

(character(1) | NULL)
The predict type, default NULL.

clone_graph

(logical(1))
Whether to clone the graph, default TRUE.

Value

RecursiveForecaster.

Examples

library(mlr3pipelines)

task = tsk("airpassengers")
split = partition(task, ratio = 0.8)

# simple: wrap a regression learner with lag features
flrn = recursive_forecaster(lrn("regr.rpart"), lags = 1:3)
flrn$train(task, split$train)
flrn$predict(task, split$test)
#> 
#> ── <PredictionFcst> for 29 observations: ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>       month row_ids truth response
#>  1958-08-01     116   505 391.9375
#>  1958-09-01     117   404 391.9375
#>  1958-10-01     118   359 391.9375
#>         ---     ---   ---      ---
#>  1960-10-01     142   461 391.9375
#>  1960-11-01     143   390 391.9375
#>  1960-12-01     144   432 391.9375

# graph: custom preprocessing pipeline
graph = po("fcst.lags", lags = 1:3) %>>% lrn("regr.rpart")
flrn = recursive_forecaster(graph)